Run download_data.Rmd and percentage_of_regional_richness.Rmd First!

fetch_city_data_for <- function(pool_name, include_city_name = F) {
  results_filename <- paste(paste(pool_name, 'city', 'richness', 'intercept', sep = "_"), "csv", sep = ".")
  results <- read_csv(results_filename)
  
  joined <- left_join(city_data, results)
  
  pool_size_col_name <- paste(pool_name, 'pool', 'size', sep = "_")
  
  required_columns <- c("response", pool_size_col_name, "population_growth", "rainfall_monthly_min", "rainfall_annual_average", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "temperature_monthly_max", "happiness_negative_effect", "happiness_positive_effect", "happiness_future_life", "number_of_biomes", "realm", "biome_name", "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", "city_includes_estuary", "region_20km_average_pop_density", "region_50km_average_pop_density", "region_100km_average_pop_density", "city_max_pop_density", "city_average_pop_density", "mean_population_exposure_to_pm2_5_2019", "region_20km_cultivated", "region_20km_urban", "region_50km_cultivated", "region_50km_urban", "region_100km_cultivated", "region_100km_urban", "region_20km_elevation_delta", "region_20km_mean_elevation", "region_50km_elevation_delta", "region_50km_mean_elevation", "region_100km_elevation_delta", "region_100km_mean_elevation", "city_elevation_delta", "city_mean_elevation", "urban", "shrubs", "permanent_water", "open_forest", "herbaceous_wetland", "herbaceous_vegetation", "cultivated", "closed_forest", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_streets", "percentage_urban_area_as_open_public_spaces_and_streets", "percentage_urban_area_as_open_public_spaces", "city_gdp_per_population")
  
  if (include_city_name) {
    required_columns <- append(c("name"), required_columns)
  }
  
  joined[,required_columns]
}
merlin_city_data <- fetch_city_data_for('merlin')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
merlin_city_data
library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
library(reshape2)
library(rpart)
library(ggplot2)

Attaching package: ‘ggplot2’

The following object is masked from ‘package:randomForest’:

    margin
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.2     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::combine()  masks randomForest::combine()
x dplyr::filter()   masks stats::filter()
x dplyr::lag()      masks stats::lag()
x ggplot2::margin() masks randomForest::margin()
merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |     3.71    20.58 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    3.979    22.07 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.115    22.83 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.001    22.19 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.161    23.08 |
merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    3.902    21.65 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    3.999    22.19 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    3.921    21.75 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    3.971    22.03 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.177    23.17 |
merlin_city_data_fixed
randomForest(response ~ ., merlin_city_data_fixed)

Call:
 randomForest(formula = response ~ ., data = merlin_city_data_fixed) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 17

          Mean of squared residuals: 16.40603
                    % Var explained: 8.99
select_variables_from_random_forest(merlin_city_data_fixed)
 [1] "merlin_pool_size"                                        "realm"                                                   "biome_name"                                             
 [4] "rainfall_monthly_min"                                    "temperature_annual_average"                              "happiness_positive_effect"                              
 [7] "region_20km_elevation_delta"                             "percentage_urban_area_as_open_public_spaces"             "region_20km_urban"                                      
[10] "region_50km_elevation_delta"                             "temperature_monthly_min"                                 "region_20km_cultivated"                                 
[13] "permanent_water"                                         "region_50km_urban"                                       "region_100km_cultivated"                                
[16] "shrubs"                                                  "city_gdp_per_population"                                 "region_50km_cultivated"                                 
[19] "region_100km_elevation_delta"                            "happiness_negative_effect"                               "region_100km_urban"                                     
[22] "region_50km_average_pop_density"                         "region_20km_average_pop_density"                         "share_of_population_within_400m_of_open_space"          
[25] "rainfall_annual_average"                                 "city_average_pop_density"                                "herbaceous_wetland"                                     
[28] "temperature_monthly_max"                                 "region_100km_average_pop_density"                        "city_mean_elevation"                                    
[31] "mean_population_exposure_to_pm2_5_2019"                  "rainfall_monthly_max"                                    "happiness_future_life"                                  
[34] "city_max_pop_density"                                    "region_50km_mean_elevation"                              "cultivated"                                             
[37] "region_20km_mean_elevation"                              "region_100km_mean_elevation"                             "urban"                                                  
[40] "population_growth"                                       "percentage_urban_area_as_open_public_spaces_and_streets" "open_forest"                                            
[43] "percentage_urban_area_as_streets"                        "closed_forest"                                          
select_variables_from_random_forest(merlin_city_data_fixed_single_scale)
 [1] "merlin_pool_size"                              "realm"                                         "biome_name"                                   
 [4] "temperature_annual_average"                    "happiness_positive_effect"                     "region_20km_elevation_delta"                  
 [7] "percentage_urban_area_as_open_public_spaces"   "rainfall_monthly_min"                          "permanent_water"                              
[10] "temperature_monthly_min"                       "region_20km_urban"                             "shrubs"                                       
[13] "region_20km_cultivated"                        "happiness_negative_effect"                     "share_of_population_within_400m_of_open_space"
[16] "temperature_monthly_max"                       "rainfall_monthly_max"                          "rainfall_annual_average"                      
[19] "happiness_future_life"                         "city_max_pop_density"                          "city_mean_elevation"                          
[22] "city_elevation_delta"                          "cultivated"                                    "population_growth"                            
[25] "region_50km_mean_elevation"                    "percentage_urban_area_as_streets"              "open_forest"                                  
[28] "closed_forest"                                
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])
[1] "Mean  18.3605779376349 , SD:  0.199971871406664 , Mean + SD:  18.5605498090416"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm")])
[1] "Mean  13.8483857782536 , SD:  0.167233775821336 , Mean + SD:  14.015619554075"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name")])
[1] "Mean  14.1417551442684 , SD:  0.160688673425095 , Mean + SD:  14.3024438176934"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average")])
[1] "Mean  14.5103292900374 , SD:  0.234962153093279 , Mean + SD:  14.7452914431307"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect")])
[1] "Mean  14.7574657636192 , SD:  0.243199251750122 , Mean + SD:  15.0006650153693"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta")])
[1] "Mean  14.9256981639764 , SD:  0.240704529916054 , Mean + SD:  15.1664026938924"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces")])
[1] "Mean  14.788101572417 , SD:  0.263257912139604 , Mean + SD:  15.0513594845566"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])
[1] "Mean  14.708125023514 , SD:  0.244893814930232 , Mean + SD:  14.9530188384442"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water")])
[1] "Mean  14.71026136053 , SD:  0.251430414275647 , Mean + SD:  14.9616917748057"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min")])
[1] "Mean  15.0471745956683 , SD:  0.215420444110583 , Mean + SD:  15.2625950397789"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban")])
[1] "Mean  15.1350022423199 , SD:  0.339891929354689 , Mean + SD:  15.4748941716745"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs")])
[1] "Mean  15.1353707728019 , SD:  0.319439216090642 , Mean + SD:  15.4548099888925"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated")])
[1] "Mean  15.169974474564 , SD:  0.270058572377652 , Mean + SD:  15.4400330469417"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect")])
[1] "Mean  15.2117893322605 , SD:  0.300179792841188 , Mean + SD:  15.5119691251017"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space")])
[1] "Mean  15.2800537211106 , SD:  0.287378331903091 , Mean + SD:  15.5674320530137"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max")])
[1] "Mean  15.4080077907785 , SD:  0.299436746178816 , Mean + SD:  15.7074445369573"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max")])
[1] "Mean  15.6416908542101 , SD:  0.24366735672925 , Mean + SD:  15.8853582109394"
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max", "rainfall_annual_average")])
[1] "Mean  15.6107006616448 , SD:  0.240063905776268 , Mean + SD:  15.850764567421"

“merlin_pool_size”, “realm”

birdlife_city_data <- fetch_city_data_for('birdlife')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
birdlife_city_data <- fetch_city_data_for('birdlife')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
birdlife_city_data
birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.546    87.80 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |     5.56    88.01 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.312    84.08 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.467    86.55 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.482    86.77 |
birdlife_city_data_fixed
select_variables_from_random_forest(birdlife_city_data_fixed)
 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "biome_name"                                              "region_20km_average_pop_density"                         "rainfall_monthly_min"                                   
 [7] "percentage_urban_area_as_open_public_spaces"             "region_50km_cultivated"                                  "permanent_water"                                        
[10] "region_50km_average_pop_density"                         "rainfall_monthly_max"                                    "mean_population_exposure_to_pm2_5_2019"                 
[13] "temperature_monthly_min"                                 "shrubs"                                                  "temperature_annual_average"                             
[16] "region_100km_average_pop_density"                        "region_100km_urban"                                      "region_20km_cultivated"                                 
[19] "percentage_urban_area_as_open_public_spaces_and_streets" "region_20km_elevation_delta"                             "share_of_population_within_400m_of_open_space"          
[22] "region_20km_urban"                                       "city_average_pop_density"                                "happiness_future_life"                                  
[25] "region_50km_elevation_delta"                             "region_50km_urban"                                       "open_forest"                                            
[28] "percentage_urban_area_as_streets"                        "temperature_monthly_max"                                 "realm"                                                  
[31] "city_max_pop_density"                                    "city_elevation_delta"                                    "rainfall_annual_average"                                
[34] "city_gdp_per_population"                                 "cultivated"                                              "happiness_negative_effect"                              
[37] "region_100km_mean_elevation"                             "region_50km_mean_elevation"                              "city_mean_elevation"                                    
[40] "closed_forest"                                           "happiness_positive_effect"                               "herbaceous_wetland"                                     
[43] "urban"                                                   "herbaceous_vegetation"                                  
select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)
 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "percentage_urban_area_as_open_public_spaces"             "biome_name"                                              "rainfall_monthly_min"                                   
 [7] "region_20km_average_pop_density"                         "permanent_water"                                         "rainfall_monthly_max"                                   
[10] "temperature_annual_average"                              "temperature_monthly_min"                                 "mean_population_exposure_to_pm2_5_2019"                 
[13] "region_100km_urban"                                      "shrubs"                                                  "region_20km_elevation_delta"                            
[16] "percentage_urban_area_as_open_public_spaces_and_streets" "share_of_population_within_400m_of_open_space"           "realm"                                                  
[19] "city_average_pop_density"                                "open_forest"                                             "happiness_future_life"                                  
[22] "city_elevation_delta"                                    "temperature_monthly_max"                                 "rainfall_annual_average"                                
[25] "percentage_urban_area_as_streets"                        "city_gdp_per_population"                                 "cultivated"                                             
[28] "happiness_negative_effect"                               "closed_forest"                                           "region_50km_mean_elevation"                             
[31] "city_mean_elevation"                                     "herbaceous_wetland"                                      "urban"                                                  
[34] "herbaceous_vegetation"                                  
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])
[1] "Mean  6.35474438479356 , SD:  0.0712540254137255 , Mean + SD:  6.42599841020729"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size")])
[1] "Mean  5.53431593845832 , SD:  0.0808938134022798 , Mean + SD:  5.6152097518606"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated")])
[1] "Mean  5.03645460544453 , SD:  0.080993921046237 , Mean + SD:  5.11744852649077"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name")])
[1] "Mean  5.01181482503518 , SD:  0.0829095109876754 , Mean + SD:  5.09472433602285"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min")])
[1] "Mean  4.97549471316348 , SD:  0.0685833826522881 , Mean + SD:  5.04407809581577"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density")])
[1] "Mean  4.86486157758304 , SD:  0.0978544677155927 , Mean + SD:  4.96271604529863"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water")])
[1] "Mean  4.75503603367959 , SD:  0.0820218992111515 , Mean + SD:  4.83705793289075"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max")])
[1] "Mean  4.83291787856041 , SD:  0.0877474150947955 , Mean + SD:  4.9206652936552"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average")])
[1] "Mean  4.89459108748752 , SD:  0.0675800594452371 , Mean + SD:  4.96217114693276"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min")])
[1] "Mean  4.87244265396031 , SD:  0.0892281563604926 , Mean + SD:  4.9616708103208"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019")])
[1] "Mean  4.83199994339927 , SD:  0.0822602681223139 , Mean + SD:  4.91426021152158"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban")])
[1] "Mean  4.82180678041379 , SD:  0.0733987377154572 , Mean + SD:  4.89520551812925"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs")])
[1] "Mean  4.88760637080854 , SD:  0.0708285469155194 , Mean + SD:  4.95843491772406"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta")])
[1] "Mean  4.92024268589129 , SD:  0.102386290093399 , Mean + SD:  5.02262897598469"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets")])
[1] "Mean  4.91143919034019 , SD:  0.0837468464163517 , Mean + SD:  4.99518603675654"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])
[1] "Mean  4.96416771734588 , SD:  0.0825668458432887 , Mean + SD:  5.04673456318917"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm")])
[1] "Mean  4.98107377749941 , SD:  0.0814576329581648 , Mean + SD:  5.06253141045758"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density")])
[1] "Mean  4.9987069406936 , SD:  0.0742949984686252 , Mean + SD:  5.07300193916222"
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density", "open_forest", "happiness_future_life")])
[1] "Mean  5.02149734174997 , SD:  0.0854499582879466 , Mean + SD:  5.10694730003792"

“population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water”

either_city_data <- fetch_city_data_for('either')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
either_city_data
either_city_data_fixed <- rfImpute(response ~ ., either_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.824    94.87 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.613    90.71 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.655    91.54 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.605    90.56 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.581    90.08 |
either_city_data_fixed
select_variables_from_random_forest(either_city_data_fixed)
 [1] "either_pool_size"                                        "population_growth"                                       "region_100km_cultivated"                                
 [4] "region_20km_average_pop_density"                         "realm"                                                   "region_50km_cultivated"                                 
 [7] "region_50km_average_pop_density"                         "biome_name"                                              "shrubs"                                                 
[10] "rainfall_monthly_min"                                    "region_100km_average_pop_density"                        "permanent_water"                                        
[13] "region_20km_cultivated"                                  "temperature_monthly_min"                                 "region_50km_elevation_delta"                            
[16] "region_20km_urban"                                       "mean_population_exposure_to_pm2_5_2019"                  "region_20km_elevation_delta"                            
[19] "percentage_urban_area_as_open_public_spaces"             "city_average_pop_density"                                "happiness_future_life"                                  
[22] "rainfall_monthly_max"                                    "temperature_annual_average"                              "temperature_monthly_max"                                
[25] "region_100km_urban"                                      "region_50km_urban"                                       "cultivated"                                             
[28] "share_of_population_within_400m_of_open_space"           "city_max_pop_density"                                    "city_elevation_delta"                                   
[31] "city_mean_elevation"                                     "herbaceous_wetland"                                      "rainfall_annual_average"                                
[34] "region_100km_elevation_delta"                            "city_gdp_per_population"                                 "region_20km_mean_elevation"                             
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "region_50km_mean_elevation"                              "region_100km_mean_elevation"                            
[40] "happiness_negative_effect"                               "open_forest"                                             "urban"                                                  
[43] "happiness_positive_effect"                               "herbaceous_vegetation"                                   "percentage_urban_area_as_streets"                       
[46] "closed_forest"                                          
select_variables_from_random_forest(either_city_data_fixed_single_scale)
 [1] "either_pool_size"                                        "population_growth"                                       "region_100km_cultivated"                                
 [4] "region_20km_average_pop_density"                         "realm"                                                   "biome_name"                                             
 [7] "rainfall_monthly_min"                                    "shrubs"                                                  "temperature_monthly_min"                                
[10] "permanent_water"                                         "percentage_urban_area_as_open_public_spaces"             "region_20km_urban"                                      
[13] "region_50km_elevation_delta"                             "mean_population_exposure_to_pm2_5_2019"                  "city_average_pop_density"                               
[16] "rainfall_monthly_max"                                    "happiness_future_life"                                   "cultivated"                                             
[19] "share_of_population_within_400m_of_open_space"           "city_elevation_delta"                                    "city_max_pop_density"                                   
[22] "rainfall_annual_average"                                 "city_mean_elevation"                                     "percentage_urban_area_as_open_public_spaces_and_streets"
[25] "temperature_monthly_max"                                 "region_20km_mean_elevation"                              "open_forest"                                            
[28] "happiness_negative_effect"                               "urban"                                                   "percentage_urban_area_as_streets"                       
[31] "closed_forest"                                          
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])
[1] "Mean  4.69133292981281 , SD:  0.0489285631824466 , Mean + SD:  4.74026149299525"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth")])
[1] "Mean  4.18668186942246 , SD:  0.0652412455486544 , Mean + SD:  4.25192311497111"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated")])
[1] "Mean  4.08678736410676 , SD:  0.05782944992901 , Mean + SD:  4.14461681403577"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density")])
[1] "Mean  3.7612122389608 , SD:  0.0626910363388465 , Mean + SD:  3.82390327529965"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm")])
[1] "Mean  3.62757829197622 , SD:  0.0641414031735027 , Mean + SD:  3.69171969514973"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name")])
[1] "Mean  3.88541888033384 , SD:  0.0697761912903623 , Mean + SD:  3.9551950716242"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min")])
[1] "Mean  3.94442754211899 , SD:  0.0705357146629719 , Mean + SD:  4.01496325678196"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs")])
[1] "Mean  3.94268869246952 , SD:  0.0683286721896993 , Mean + SD:  4.01101736465922"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min")])
[1] "Mean  3.91638012322931 , SD:  0.058327290718483 , Mean + SD:  3.97470741394779"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water")])
[1] "Mean  3.97807710896339 , SD:  0.0819372375299245 , Mean + SD:  4.06001434649332"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces")])
[1] "Mean  4.08096128871886 , SD:  0.0742590817487434 , Mean + SD:  4.15522037046761"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban")])
[1] "Mean  4.10276372102361 , SD:  0.0793013544355574 , Mean + SD:  4.18206507545916"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta")])
[1] "Mean  4.14360043109383 , SD:  0.0571122481729091 , Mean + SD:  4.20071267926674"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019")])
[1] "Mean  4.17420426151807 , SD:  0.0800438347270345 , Mean + SD:  4.25424809624511"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])
[1] "Mean  4.20142417235852 , SD:  0.0534400086677398 , Mean + SD:  4.25486418102626"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max")])
[1] "Mean  4.23495161199434 , SD:  0.0810189022929709 , Mean + SD:  4.31597051428731"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life")])
[1] "Mean  4.25511343099509 , SD:  0.0777504142418531 , Mean + SD:  4.33286384523695"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated")])
[1] "Mean  4.27187737013208 , SD:  0.0659717347812921 , Mean + SD:  4.33784910491338"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space")])
[1] "Mean  4.32785556380064 , SD:  0.0915334527831198 , Mean + SD:  4.41938901658376"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta")])
[1] "Mean  4.33688765161498 , SD:  0.0687985208297185 , Mean + SD:  4.4056861724447"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta", "city_max_pop_density")])
[1] "Mean  4.35933973615371 , SD:  0.0853454117772923 , Mean + SD:  4.444685147931"

“either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm”

both_city_data <- fetch_city_data_for('both')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
both_city_data
both_city_data_fixed <- rfImpute(response ~ ., both_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.32    99.56 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.46    94.36 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.88    96.87 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.65    95.52 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.91    97.08 |
both_city_data_fixed
select_variables_from_random_forest(both_city_data_fixed)
 [1] "both_pool_size"                                          "temperature_annual_average"                              "temperature_monthly_min"                                
 [4] "permanent_water"                                         "happiness_negative_effect"                               "region_20km_urban"                                      
 [7] "region_100km_cultivated"                                 "region_50km_cultivated"                                  "realm"                                                  
[10] "region_20km_cultivated"                                  "rainfall_monthly_min"                                    "region_50km_elevation_delta"                            
[13] "population_growth"                                       "shrubs"                                                  "region_100km_elevation_delta"                           
[16] "region_20km_average_pop_density"                         "region_100km_urban"                                      "biome_name"                                             
[19] "region_20km_elevation_delta"                             "region_50km_urban"                                       "percentage_urban_area_as_open_public_spaces"            
[22] "city_average_pop_density"                                "city_gdp_per_population"                                 "region_50km_average_pop_density"                        
[25] "open_forest"                                             "herbaceous_wetland"                                      "cultivated"                                             
[28] "region_100km_average_pop_density"                        "region_20km_mean_elevation"                              "share_of_population_within_400m_of_open_space"          
[31] "mean_population_exposure_to_pm2_5_2019"                  "city_elevation_delta"                                    "region_50km_mean_elevation"                             
[34] "happiness_future_life"                                   "happiness_positive_effect"                               "rainfall_monthly_max"                                   
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "herbaceous_vegetation"                                   "temperature_monthly_max"                                
[40] "percentage_urban_area_as_streets"                        "rainfall_annual_average"                                 "urban"                                                  
[43] "closed_forest"                                          
select_variables_from_random_forest(both_city_data_fixed_single_scale)
 [1] "both_pool_size"                                "temperature_annual_average"                    "temperature_monthly_min"                      
 [4] "permanent_water"                               "happiness_negative_effect"                     "region_20km_urban"                            
 [7] "rainfall_monthly_min"                          "realm"                                         "region_100km_cultivated"                      
[10] "region_50km_elevation_delta"                   "population_growth"                             "percentage_urban_area_as_open_public_spaces"  
[13] "shrubs"                                        "biome_name"                                    "region_20km_average_pop_density"              
[16] "city_mean_elevation"                           "city_gdp_per_population"                       "share_of_population_within_400m_of_open_space"
[19] "cultivated"                                    "open_forest"                                   "region_20km_mean_elevation"                   
[22] "rainfall_monthly_max"                          "temperature_monthly_max"                       "rainfall_annual_average"                      
[25] "percentage_urban_area_as_streets"              "closed_forest"                                 "urban"                                        
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size")])
[1] "Mean  17.0501746482574 , SD:  0.180832956779973 , Mean + SD:  17.2310076050374"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average")])
[1] "Mean  14.1770117377572 , SD:  0.12741540946317 , Mean + SD:  14.3044271472204"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min")])
[1] "Mean  13.9947226485825 , SD:  0.193418308082352 , Mean + SD:  14.1881409566648"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water")])
[1] "Mean  13.980419606819 , SD:  0.198277385993035 , Mean + SD:  14.1786969928121"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect")])
[1] "Mean  14.250869948595 , SD:  0.205833621996072 , Mean + SD:  14.4567035705911"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban")])
[1] "Mean  13.831253031623 , SD:  0.267164827147086 , Mean + SD:  14.0984178587701"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min")])
[1] "Mean  14.0176076470363 , SD:  0.233933915226803 , Mean + SD:  14.2515415622631"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm")])
[1] "Mean  13.9678429656754 , SD:  0.27500268619645 , Mean + SD:  14.2428456518719"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated"),])
[1] "Mean  13.8133557218721 , SD:  0.208047207294211 , Mean + SD:  14.0214029291663"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta"),])
[1] "Mean  14.1587620560898 , SD:  0.265407381711604 , Mean + SD:  14.4241694378014"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth"),])
[1] "Mean  14.3695231260649 , SD:  0.277971868419252 , Mean + SD:  14.6474949944841"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces"),])
[1] "Mean  14.6989193629512 , SD:  0.281371086172517 , Mean + SD:  14.9802904491237"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs"),])
[1] "Mean  14.6255340760297 , SD:  0.287689760117654 , Mean + SD:  14.9132238361473"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name"),])
[1] "Mean  14.6561287164134 , SD:  0.265987324658439 , Mean + SD:  14.9221160410718"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density"),])
[1] "Mean  14.7323907720158 , SD:  0.194408848363496 , Mean + SD:  14.9267996203793"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation"),])
[1] "Mean  14.7915916869301 , SD:  0.298364380477512 , Mean + SD:  15.0899560674076"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population"),])
[1] "Mean  14.9134394356877 , SD:  0.224050531561373 , Mean + SD:  15.137489967249"
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population", "share_of_population_within_400m_of_open_space"),])
[1] "Mean  15.0099497484692 , SD:  0.2218461976947 , Mean + SD:  15.2317959461639"

“both_pool_size”, “temperature_annual_average”, “happiness_negative_effect”

So….
“merlin_pool_size”, “realm” “population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water” “either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm” “both_pool_size”, “temperature_annual_average”, “temperature_monthly_min”
```r summary(lm(response ~ merlin_pool_size, merlin_city_data_fixed))
```
```
Call: lm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed)
Residuals: Min 1Q Median 3Q Max -8.3644 -2.2493 -0.3649 1.7804 15.4604
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.205975 0.920945 6.739 4.23e-10 merlin_pool_size -0.022439 0.003134 -7.160 4.71e-11

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.641 on 135 degrees of freedom Multiple R-squared: 0.2752, Adjusted R-squared: 0.2698 F-statistic: 51.26 on 1 and 135 DF, p-value: 4.707e-11




<!-- rnb-output-end -->

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```r
summary(lm(response ~ birdlife_pool_size, birdlife_city_data_fixed))

Call:
lm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.140 -1.330 -0.313  1.034  9.156 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         2.602931   0.625873   4.159 5.65e-05 ***
birdlife_pool_size -0.008789   0.002000  -4.395 2.23e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.368 on 135 degrees of freedom
Multiple R-squared:  0.1252,    Adjusted R-squared:  0.1187 
F-statistic: 19.31 on 1 and 135 DF,  p-value: 2.225e-05
summary(lm(response ~ either_pool_size, either_city_data_fixed))

Call:
lm(formula = response ~ either_pool_size, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8488 -1.0658 -0.3811  0.8665  6.5921 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       3.250304   0.584389   5.562 1.38e-07 ***
either_pool_size -0.009005   0.001546  -5.825 3.99e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.031 on 135 degrees of freedom
Multiple R-squared:  0.2008,    Adjusted R-squared:  0.1949 
F-statistic: 33.92 on 1 and 135 DF,  p-value: 3.99e-08
summary(lm(response ~ both_pool_size, both_city_data_fixed))

Call:
lm(formula = response ~ both_pool_size, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.9674 -2.7370 -0.3475  1.8439 10.3398 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     5.261657   0.982371   5.356 3.56e-07 ***
both_pool_size -0.024842   0.004396  -5.651 9.08e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.667 on 135 degrees of freedom
Multiple R-squared:  0.1913,    Adjusted R-squared:  0.1853 
F-statistic: 31.94 on 1 and 135 DF,  p-value: 9.076e-08

summary(lm(response ~ region_100km_cultivated, merlin_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7405 -2.8276 -0.5911  1.5098 18.0590 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.6281     0.5172  -1.214   0.2267  
region_100km_cultivated   2.3444     1.3805   1.698   0.0918 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.232 on 135 degrees of freedom
Multiple R-squared:  0.02092,   Adjusted R-squared:  0.01366 
F-statistic: 2.884 on 1 and 135 DF,  p-value: 0.09176
summary(lm(response ~ region_100km_cultivated, birdlife_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4506 -1.5884 -0.3702  1.3865  9.9581 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.6226     0.3002  -2.074  0.04001 * 
region_100km_cultivated   2.3237     0.8013   2.900  0.00436 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.457 on 135 degrees of freedom
Multiple R-squared:  0.05864,   Adjusted R-squared:  0.05167 
F-statistic: 8.409 on 1 and 135 DF,  p-value: 0.004359
summary(lm(response ~ region_100km_cultivated, either_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6522 -1.4255 -0.2114  0.9771  6.3724 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.5459     0.2698  -2.024  0.04499 * 
region_100km_cultivated   2.0373     0.7200   2.830  0.00537 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.207 on 135 degrees of freedom
Multiple R-squared:  0.05599,   Adjusted R-squared:  0.049 
F-statistic: 8.008 on 1 and 135 DF,  p-value: 0.00537
summary(lm(response ~ region_100km_cultivated, both_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = both_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-8.439 -2.791 -0.689  1.898 12.088 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.7221     0.4908  -1.471   0.1436  
region_100km_cultivated   2.6951     1.3099   2.057   0.0416 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.016 on 135 degrees of freedom
Multiple R-squared:  0.0304,    Adjusted R-squared:  0.02322 
F-statistic: 4.233 on 1 and 135 DF,  p-value: 0.04157
ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_100km_cultivated), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_100km_cultivated), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_100km_cultivated), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_100km_cultivated), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = population_growth, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = population_growth, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = population_growth, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = population_growth, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_20km_average_pop_density), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_20km_average_pop_density), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_20km_average_pop_density), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_20km_average_pop_density), both_city_data_fixed, color = "purple")

summary(lm(response ~ population_growth, merlin_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2751 -2.8391 -0.4272  1.4837 18.4058 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.094091   0.524516   0.179    0.858
population_growth -0.001479   0.005915  -0.250    0.803

Residual standard error: 4.276 on 135 degrees of freedom
Multiple R-squared:  0.0004627, Adjusted R-squared:  -0.006941 
F-statistic: 0.0625 on 1 and 135 DF,  p-value: 0.803
summary(lm(response ~ population_growth, birdlife_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.085 -1.538 -0.459  1.240 10.226 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.231365   0.309332   0.748    0.456
population_growth -0.003636   0.003489  -1.042    0.299

Residual standard error: 2.522 on 135 degrees of freedom
Multiple R-squared:  0.007984,  Adjusted R-squared:  0.0006359 
F-statistic: 1.087 on 1 and 135 DF,  p-value: 0.2991
summary(lm(response ~ population_growth, either_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1409 -1.3284 -0.1829  0.8324  6.7919 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.113195   0.278318   0.407    0.685
population_growth -0.001779   0.003139  -0.567    0.572

Residual standard error: 2.269 on 135 degrees of freedom
Multiple R-squared:  0.002374,  Adjusted R-squared:  -0.005016 
F-statistic: 0.3213 on 1 and 135 DF,  p-value: 0.5718
summary(lm(response ~ population_growth, both_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.1143 -2.5568 -0.7818  2.1289 12.4621 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.188410   0.499736   0.377    0.707
population_growth -0.002961   0.005636  -0.525    0.600

Residual standard error: 4.074 on 135 degrees of freedom
Multiple R-squared:  0.002041,  Adjusted R-squared:  -0.005351 
F-statistic: 0.2761 on 1 and 135 DF,  p-value: 0.6001
summary(lm(response ~ rainfall_monthly_min, merlin_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2835 -2.9452 -0.4893  1.4983 18.2505 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.191459   0.491332   0.390    0.697
rainfall_monthly_min -0.007481   0.012853  -0.582    0.562

Residual standard error: 4.272 on 135 degrees of freedom
Multiple R-squared:  0.002503,  Adjusted R-squared:  -0.004886 
F-statistic: 0.3387 on 1 and 135 DF,  p-value: 0.5615
summary(lm(response ~ rainfall_monthly_min, birdlife_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0114 -1.4084 -0.4231  1.3632 10.6767 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.244199   0.289526   0.843     0.40
rainfall_monthly_min -0.009541   0.007574  -1.260     0.21

Residual standard error: 2.517 on 135 degrees of freedom
Multiple R-squared:  0.01162,   Adjusted R-squared:  0.004298 
F-statistic: 1.587 on 1 and 135 DF,  p-value: 0.2099
summary(lm(response ~ rainfall_monthly_min, either_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1121 -1.3720 -0.2964  0.8111  6.5298 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.219743   0.259756   0.846    0.399
rainfall_monthly_min -0.008586   0.006795  -1.264    0.209

Residual standard error: 2.258 on 135 degrees of freedom
Multiple R-squared:  0.01169,   Adjusted R-squared:  0.004367 
F-statistic: 1.597 on 1 and 135 DF,  p-value: 0.2086
summary(lm(response ~ rainfall_monthly_min, both_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0991 -2.8506 -0.8491  1.9009 12.2257 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.30602    0.46742   0.655    0.514
rainfall_monthly_min -0.01196    0.01223  -0.978    0.330

Residual standard error: 4.064 on 135 degrees of freedom
Multiple R-squared:  0.007033,  Adjusted R-squared:  -0.0003223 
F-statistic: 0.9562 on 1 and 135 DF,  p-value: 0.3299
ggplot() + 
  geom_point(aes(x = rainfall_monthly_min, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = rainfall_monthly_min, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = rainfall_monthly_min, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = rainfall_monthly_min, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = temperature_annual_average, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = temperature_annual_average, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = temperature_annual_average, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = temperature_annual_average, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = happiness_negative_effect, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = happiness_negative_effect, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = happiness_negative_effect, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = happiness_negative_effect, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), merlin_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), birdlife_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), either_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), both_city_data_fixed)

summary(lm(response ~ biome_name, merlin_city_data_fixed))

Call:
lm(formula = response ~ biome_name, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7663 -2.4594 -0.4676  2.1272 18.4309 

Coefficients:
                                                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                                                         -3.2599     4.2666  -0.764   0.4463  
biome_nameDeserts & Xeric Shrublands                                 3.1836     4.4563   0.714   0.4763  
biome_nameFlooded Grasslands & Savannas                              0.6618     5.2255   0.127   0.8994  
biome_nameMangroves                                                  9.3150     5.2255   1.783   0.0771 .
biome_nameMediterranean Forests, Woodlands & Scrub                   3.2643     4.4066   0.741   0.4602  
biome_nameMontane Grasslands & Shrublands                            1.5344     5.2255   0.294   0.7695  
biome_nameTemperate Broadleaf & Mixed Forests                        3.2942     4.3328   0.760   0.4485  
biome_nameTemperate Conifer Forests                                  3.3572     5.2255   0.642   0.5218  
biome_nameTemperate Grasslands, Savannas & Shrublands                4.3835     4.6739   0.938   0.3501  
biome_nameTropical & Subtropical Coniferous Forests                  7.4846     5.2255   1.432   0.1546  
biome_nameTropical & Subtropical Dry Broadleaf Forests               3.7631     4.4164   0.852   0.3958  
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands   5.9138     4.6739   1.265   0.2081  
biome_nameTropical & Subtropical Moist Broadleaf Forests             2.4622     4.3148   0.571   0.5693  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.267 on 124 degrees of freedom
Multiple R-squared:  0.08597,   Adjusted R-squared:  -0.002489 
F-statistic: 0.9719 on 12 and 124 DF,  p-value: 0.4793
In Summary

Response is related to number of species in regional pool, the more species, the less the percentage of species in the city. Indicating a fixed number of species are able to move into cities. The size of the regional pool is correlated with both the amount of urban and cultivated land cover, both reduce species in the regional pool.

Response is also lower in wet biomes and areas of the world, this is seen through the higher rainfall in the month with least rainfall in the year, and lower percentages in wetter biomes such as flooded grasslands and moist broadleaf forests.

Finally cities with a higher proportion of green public space are less likely to have a low response.

merlin_city_data_2 <- fetch_city_data_for('merlin', T)
birdlife_city_data_2 <- fetch_city_data_for('birdlife', T)
merlin_city_data$residuals_pool_size
         1          2          3          4          5          6          7          8          9         10         11         12         13         14         15         16         17 
 3.6346589 -1.3808445  1.0649349 -3.4355790 -4.3126647 -0.7438113 -7.7336701 -2.4387017  1.4453341 -1.0023679 -3.6997976 -1.3953352  7.7497245 -0.8622274  0.5366795  4.5265798  1.0617137 
        18         19         20         21         22         23         24         25         26         27         28         29         30         31         32         33         34 
-0.2523214  4.7814815 -3.1223646  1.7091096 -3.6108085 -0.1397141 15.4603887 -0.6266225 -3.0909248  1.0182028  1.7201615 -2.7031265  8.5064874  8.8776781  7.0569196  0.7062920 -1.2592215 
        35         36         37         38         39         40         41         42         43         44         45         46         47         48         49         50         51 
 1.2336978  0.1383472  7.0252049  2.2268554 -2.0718664  1.9814449  3.8241566  7.2649549  6.1520103  3.4917821 -0.1258741  1.8033208  0.4220553 -2.0346183 -3.6731730 -0.6081145 -4.7650229 
        52         53         54         55         56         57         58         59         60         61         62         63         64         65         66         67         68 
-0.6374446  5.3381301 -0.5829402 -5.3003161 -1.6870541  3.8366707  4.8978947  0.2824830 -1.4631580 -1.6682595 -3.0684045  1.8602911  2.1229221 -3.4328909  0.7519428 -2.0768581 -1.3890451 
        69         70         71         72         73         74         75         76         77         78         79         80         81         82         83         84         85 
-5.0351403 -2.1185232 -0.7391181  2.2308499  0.1957247  1.8741271 -3.0258162 -3.2283923 -5.4035769 -2.1190007 -5.5415234 -1.3942752  2.2271298  2.4763802  0.9951147  7.2106299 -3.6705115 
        86         87         88         89         90         91         92         93         94         95         96         97         98         99        100        101        102 
-4.0562506 -2.6050825  2.2253612 -0.6344070 -1.7984823 -1.3325552 -2.2520966  6.5027968 -0.4457886  1.0550630 -3.3616225 -4.5509836  0.4535341  0.5300748 -0.9801669  2.9973939  1.3750704 
       103        104        105        106        107        108        109        110        111        112        113        114        115        116        117        118        119 
 1.5953003 -4.9345018 -2.4455672  7.0465516 -1.7276856 -0.3215003 -0.4133246  2.9320579 -2.2492757 -3.4825074 -5.7859859 -1.4772208  1.8647427 -5.5206398  1.8961396  0.3861443 -2.6258278 
       120        121        122        123        124        125        126        127        128        129        130        131        132        133        134        135        136 
-1.2608866  7.3346754 -0.3648861 -0.9025271 -8.3644404 -0.2970551 -1.8090516 -4.4254561  0.5833549 -4.5589025  1.1898920 -0.1771279  0.4107215 -0.2915643  1.0268999  1.7803945 -0.9484046 
       137 
 0.1661597 
merlin_by_all.lm <- lm(response ~ birdlife_pool_size + realm + population_growth + region_100km_cultivated + percentage_urban_area_as_open_public_spaces + biome_name + rainfall_monthly_min + region_20km_average_pop_density+ permanent_water + population_growth + temperature_annual_average + temperature_monthly_min, data = merlin_city_data_fixed)
Error in eval(predvars, data, env) : 
  object 'birdlife_pool_size' not found
merlin_by_preferred.lm <- lm(response ~ merlin_pool_size + biome_name + rainfall_monthly_min, data = merlin_city_data_fixed)
merlin_city_data$residuals_preferred <- resid(merlin_by_preferred.lm)

birdlife_by_preferred.lm <- lm(response ~ birdlife_pool_size + biome_name + rainfall_monthly_min, data = birdlife_city_data_fixed)
birdlife_city_data$residuals_preferred <- resid(birdlife_by_preferred.lm)
write_csv(ordered_cities, "city_effect_residuals.csv")
merlin_city_data$name <- merlin_city_data_2$name
plot_merlin_poolsize <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_pool_size), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size")
plot_merlin_poolsize
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 123 unlabeled data points (too many overlaps). Consider increasing max.overlaps

birdlife_city_data$name <- birdlife_city_data_2$name
plot_birdlife_poolsize <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_pool_size), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size")
plot_birdlife_poolsize
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 114 unlabeled data points (too many overlaps). Consider increasing max.overlaps

plot_merlin_preferred <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_preferred), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size, biome, and rainfall")
plot_merlin_preferred
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 123 unlabeled data points (too many overlaps). Consider increasing max.overlaps

plot_birdlife_preferred <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_preferred), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size, biome, and rainfall")
plot_birdlife_preferred
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 114 unlabeled data points (too many overlaps). Consider increasing max.overlaps

library(ggpubr)
plot_residuals <- ggarrange(plot_merlin_poolsize, plot_birdlife_poolsize, plot_merlin_preferred, plot_birdlife_preferred)
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
plot_residuals
Warning: ggrepel: 134 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 134 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider increasing max.overlaps

jpeg("city_effect_residuals.jpg", width = 1600, height = 1200)
plot_residuals
Warning: ggrepel: 83 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 69 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 83 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 69 unlabeled data points (too many overlaps). Consider increasing max.overlaps
dev.off()
null device 
          1 
merlin_city_data_200 <- merlin_city_data[merlin_city_data$merlin_pool_size > 190 & merlin_city_data$merlin_pool_size < 210,]
merlin_city_data_200[order(merlin_city_data_200$response), c("name", "response")]
birdlife_city_data_200 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 190 & birdlife_city_data$birdlife_pool_size < 210,]
birdlife_city_data_200[order(birdlife_city_data_200$response), c("name", "response")]
birdlife_city_data_300 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 290 & birdlife_city_data$birdlife_pool_size < 310,]
birdlife_city_data_300[order(birdlife_city_data_300$response), c("name", "response")]
merlin_city_data_300 <- merlin_city_data[merlin_city_data$merlin_pool_size > 290 & merlin_city_data$merlin_pool_size < 310,]
merlin_city_data_300[order(merlin_city_data_300$response), c("name", "response")]

ggplot(city_data_subset, aes(x = label, y = response, color = label)) + 
  geom_label_repel(aes(label = biome_name), size = 3) + geom_point() +
  theme_bw() + theme(legend.position = "none") + xlab("Pool size (Pool)") + ylab("Random Effect Response")
Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider increasing max.overlaps

table(city_data$biome_name)

                                    Boreal Forests/Taiga                               Deserts & Xeric Shrublands                            Flooded Grasslands & Savannas 
                                                       1                                                       11                                                        2 
                                               Mangroves                 Mediterranean Forests, Woodlands & Scrub                          Montane Grasslands & Shrublands 
                                                       2                                                       15                                                        2 
                     Temperate Broadleaf & Mixed Forests                                Temperate Conifer Forests              Temperate Grasslands, Savannas & Shrublands 
                                                      32                                                        2                                                        5 
               Tropical & Subtropical Coniferous Forests             Tropical & Subtropical Dry Broadleaf Forests Tropical & Subtropical Grasslands, Savannas & Shrublands 
                                                       2                                                       14                                                        5 
          Tropical & Subtropical Moist Broadleaf Forests 
                                                      44 
summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + merlin_pool_size, merlin_city_data, family = "gaussian"))

Call:
glm(formula = response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + 
    merlin_pool_size, family = "gaussian", data = merlin_city_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-8.0835  -1.9045  -0.2273   1.7119  16.1025  

Coefficients:
                                                                                                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                                                                               5.885752   1.000848   5.881 3.60e-08 ***
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Boreal Forests/Taiga                                     -5.189947   3.612126  -1.437   0.1533    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Deserts & Xeric Shrublands                                1.434085   1.257137   1.141   0.2562    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Flooded Grasslands & Savannas                            -2.670900   2.586224  -1.033   0.3038    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mangroves                                                 5.849655   2.586319   2.262   0.0255 *  
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mediterranean Forests, Woodlands & Scrub                 -0.079682   1.110329  -0.072   0.9429    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Montane Grasslands & Shrublands                           0.324356   2.601094   0.125   0.9010    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Conifer Forests                                 1.206379   2.590705   0.466   0.6423    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Grasslands, Savannas & Shrublands               2.811650   1.721678   1.633   0.1050    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Coniferous Forests                 5.225315   2.589895   2.018   0.0458 *  
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Dry Broadleaf Forests              1.775978   1.150258   1.544   0.1252    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Grasslands, Savannas & Shrublands  2.889845   1.706685   1.693   0.0929 .  
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Moist Broadleaf Forests            0.567393   0.845207   0.671   0.5033    
merlin_pool_size                                                                                                         -0.024120   0.003215  -7.503 1.09e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 12.59017)

    Null deviance: 2469.6  on 136  degrees of freedom
Residual deviance: 1548.6  on 123  degrees of freedom
AIC: 751.03

Number of Fisher Scoring iterations: 2
summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + birdlife_pool_size, birdlife_city_data, family = "gaussian"))

Call:
glm(formula = response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + 
    birdlife_pool_size, family = "gaussian", data = birdlife_city_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-5.1393  -1.3900  -0.1492   0.9716   9.4609  

Coefficients:
                                                                                                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                                                                               2.610738   0.717523   3.639 0.000402 ***
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Boreal Forests/Taiga                                     -3.270154   2.364009  -1.383 0.169076    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Deserts & Xeric Shrublands                                0.847287   0.821355   1.032 0.304297    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Flooded Grasslands & Savannas                            -1.251925   1.697245  -0.738 0.462149    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mangroves                                                 2.313444   1.719089   1.346 0.180862    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mediterranean Forests, Woodlands & Scrub                 -0.052808   0.730027  -0.072 0.942452    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Montane Grasslands & Shrublands                           1.774103   1.761046   1.007 0.315713    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Conifer Forests                                 1.175044   1.696677   0.693 0.489894    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Grasslands, Savannas & Shrublands               1.578314   1.120724   1.408 0.161565    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Coniferous Forests                 2.969675   1.708484   1.738 0.084679 .  
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Dry Broadleaf Forests              2.104071   0.770869   2.729 0.007274 ** 
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Grasslands, Savannas & Shrublands  0.669519   1.183055   0.566 0.572477    
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Moist Broadleaf Forests            0.561698   0.634580   0.885 0.377803    
birdlife_pool_size                                                                                                       -0.010901   0.002526  -4.315 3.25e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 5.414085)

    Null deviance: 865.45  on 136  degrees of freedom
Residual deviance: 665.93  on 123  degrees of freedom
AIC: 635.41

Number of Fisher Scoring iterations: 2
unique(city_data$biome_name)
 [1] Tropical & Subtropical Grasslands, Savannas & Shrublands Montane Grasslands & Shrublands                          Tropical & Subtropical Moist Broadleaf Forests          
 [4] Tropical & Subtropical Dry Broadleaf Forests             Mediterranean Forests, Woodlands & Scrub                 Temperate Broadleaf & Mixed Forests                     
 [7] Temperate Grasslands, Savannas & Shrublands              Flooded Grasslands & Savannas                            Deserts & Xeric Shrublands                              
[10] Tropical & Subtropical Coniferous Forests                Mangroves                                                Boreal Forests/Taiga                                    
[13] Temperate Conifer Forests                               
13 Levels: Boreal Forests/Taiga Deserts & Xeric Shrublands Flooded Grasslands & Savannas Mangroves Mediterranean Forests, Woodlands & Scrub ... Tropical & Subtropical Moist Broadleaf Forests
summary(glm(response ~ biome_vegetation + biome_location + biome_climate + merlin_pool_size, tmp, family = "gaussian"))

Call:
glm(formula = response ~ biome_vegetation + biome_location + 
    biome_climate + merlin_pool_size, family = "gaussian", data = tmp)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-8.0908  -1.9288  -0.1578   1.5228  16.1552  

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            -1.207549   4.010921  -0.301   0.7639    
biome_vegetationGrassland & Shrublands  0.897658   1.393377   0.644   0.5206    
biome_locationDesert                    7.462201   4.558021   1.637   0.1041    
biome_locationGlobal                    4.603752   5.033288   0.915   0.3621    
biome_locationMediterranean             5.068451   3.681711   1.377   0.1711    
biome_locationMontane                   4.526499   4.588225   0.987   0.3258    
biome_locationTemperate                 5.447927   3.616762   1.506   0.1345    
biome_locationTropical & Subtropical    8.707133   3.833935   2.271   0.0248 *  
biome_climateNormal                     1.813755   1.716024   1.057   0.2926    
biome_climateWet                       -1.210677   1.091495  -1.109   0.2695    
merlin_pool_size                       -0.023574   0.003203  -7.360 2.09e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 12.65148)

    Null deviance: 2469.6  on 136  degrees of freedom
Residual deviance: 1594.1  on 126  degrees of freedom
AIC: 749

Number of Fisher Scoring iterations: 2
summary(glm(response ~ biome_name + rainfall_monthly_min * merlin_pool_size, tmp, family = "gaussian"))

Call:
glm(formula = response ~ biome_name + rainfall_monthly_min * 
    merlin_pool_size, family = "gaussian", data = tmp)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-8.1207  -2.0357  -0.2982   1.7386  16.9663  

Coefficients:
                                                                     Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                                        -1.7651540  3.7445952  -0.471  0.63821   
biome_nameDeserts & Xeric Shrublands                                6.9199788  3.7166807   1.862  0.06505 . 
biome_nameFlooded Grasslands & Savannas                             3.1663312  4.3217462   0.733  0.46519   
biome_nameMangroves                                                11.4058602  4.3149495   2.643  0.00930 **
biome_nameMediterranean Forests, Woodlands & Scrub                  5.6330661  3.6528049   1.542  0.12566   
biome_nameMontane Grasslands & Shrublands                           5.5893414  4.3513235   1.285  0.20142   
biome_nameTemperate Broadleaf & Mixed Forests                       4.8242325  3.5815194   1.347  0.18050   
biome_nameTemperate Conifer Forests                                 6.3633304  4.3224710   1.472  0.14358   
biome_nameTemperate Grasslands, Savannas & Shrublands               7.7518254  3.8814809   1.997  0.04805 * 
biome_nameTropical & Subtropical Coniferous Forests                10.7588673  4.3372299   2.481  0.01449 * 
biome_nameTropical & Subtropical Dry Broadleaf Forests              7.4313934  3.6779487   2.021  0.04554 * 
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands  8.6265332  3.8689336   2.230  0.02761 * 
biome_nameTropical & Subtropical Moist Broadleaf Forests            5.8902326  3.5806727   1.645  0.10256   
rainfall_monthly_min                                                0.0846540  0.0403496   2.098  0.03798 * 
merlin_pool_size                                                   -0.0169689  0.0050538  -3.358  0.00105 **
rainfall_monthly_min:merlin_pool_size                              -0.0002442  0.0001253  -1.949  0.05367 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 12.34648)

    Null deviance: 2469.6  on 136  degrees of freedom
Residual deviance: 1493.9  on 121  degrees of freedom
AIC: 750.11

Number of Fisher Scoring iterations: 2
library(boot)
results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + merlin_pool_size)
boot.ci(results, type="bca")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates

CALL : 
boot.ci(boot.out = results, type = "bca")

Intervals : 
Level       BCa          
95%   ( 0.2256,  0.4651 )  
Calculations and Intervals on Original Scale
Some BCa intervals may be unstable
results

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~ 
    biome_name + merlin_pool_size)


Bootstrap Statistics :
     original     bias    std. error
t1* 0.3729407 0.03398538  0.06274843
results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + rainfall_monthly_min * merlin_pool_size)
boot.ci(results, type="bca")
Warning in norm.inter(t, adj.alpha) :
  extreme order statistics used as endpoints
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates

CALL : 
boot.ci(boot.out = results, type = "bca")

Intervals : 
Level       BCa          
95%   ( 0.2317,  0.4865 )  
Calculations and Intervals on Original Scale
Warning : BCa Intervals used Extreme Quantiles
Some BCa intervals may be unstable
results

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~ 
    biome_name + rainfall_monthly_min * merlin_pool_size)


Bootstrap Statistics :
     original     bias    std. error
t1* 0.3950763 0.04527929  0.06933429
results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ merlin_pool_size)
boot.ci(results, type="bca")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates

CALL : 
boot.ci(boot.out = results, type = "bca")

Intervals : 
Level       BCa          
95%   ( 0.1645,  0.3803 )  
Calculations and Intervals on Original Scale
results

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~ 
    merlin_pool_size)


Bootstrap Statistics :
    original      bias    std. error
t1* 0.275204 0.003607655  0.05538602
---
title: "R Notebook"
output: html_notebook
---
Run `download_data.Rmd` and `percentage_of_regional_richness.Rmd` First!

```{r}
city_data
```

```{r}
fetch_city_data_for <- function(pool_name, include_city_name = F) {
  results_filename <- paste(paste(pool_name, 'city', 'richness', 'intercept', sep = "_"), "csv", sep = ".")
  results <- read_csv(results_filename)
  
  joined <- left_join(city_data, results)
  
  pool_size_col_name <- paste(pool_name, 'pool', 'size', sep = "_")
  
  required_columns <- c("response", pool_size_col_name, "population_growth", "rainfall_monthly_min", "rainfall_annual_average", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "temperature_monthly_max", "happiness_negative_effect", "happiness_positive_effect", "happiness_future_life", "number_of_biomes", "realm", "biome_name", "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", "city_includes_estuary", "region_20km_average_pop_density", "region_50km_average_pop_density", "region_100km_average_pop_density", "city_max_pop_density", "city_average_pop_density", "mean_population_exposure_to_pm2_5_2019", "region_20km_cultivated", "region_20km_urban", "region_50km_cultivated", "region_50km_urban", "region_100km_cultivated", "region_100km_urban", "region_20km_elevation_delta", "region_20km_mean_elevation", "region_50km_elevation_delta", "region_50km_mean_elevation", "region_100km_elevation_delta", "region_100km_mean_elevation", "city_elevation_delta", "city_mean_elevation", "urban", "shrubs", "permanent_water", "open_forest", "herbaceous_wetland", "herbaceous_vegetation", "cultivated", "closed_forest", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_streets", "percentage_urban_area_as_open_public_spaces_and_streets", "percentage_urban_area_as_open_public_spaces", "city_gdp_per_population")
  
  if (include_city_name) {
    required_columns <- append(c("name"), required_columns)
  }
  
  joined[,required_columns]
}
```


```{r}
merlin_city_data <- fetch_city_data_for('merlin')
merlin_city_data
```

```{r}
library(randomForest)
library(reshape2)
library(rpart)
library(ggplot2)
library(tidyverse)
```

```{r}
merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
merlin_city_data_fixed
```


```{r}
source('./random_forest_selection_functions.R')
```

```{r}
select_variables_from_random_forest(merlin_city_data_fixed)
```

```{r}
exclude_merlin <- !names(merlin_city_data_fixed) %in% c(
  "region_50km_urban", "region_100km_urban", 
  "region_50km_elevation_delta", "region_100km_elevation_delta", 
  "region_50km_cultivated", "region_100km_cultivated", 
  "region_20km_average_pop_density", "region_100km_average_pop_density", 
  "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", 
  "region_100km_mean_elevation", "region_20km_mean_elevation")

merlin_city_data_fixed_single_scale <- merlin_city_data_fixed[,exclude_merlin]
merlin_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(merlin_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max", "rainfall_annual_average")])
```

"merlin_pool_size", "realm"


```{r}
birdlife_city_data <- fetch_city_data_for('birdlife')
birdlife_city_data
```

```{r}
birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)
birdlife_city_data_fixed
```

```{r}
select_variables_from_random_forest(birdlife_city_data_fixed)
```

```{r}
exclude_birdlife <- !names(birdlife_city_data_fixed) %in% c(
  "region_50km_cultivated", "region_20km_cultivated", 
  "region_100km_average_pop_density", "region_50km_average_pop_density", 
  "region_50km_urban", "region_20km_urban", 
  "region_100km_elevation_delta", "region_50km_elevation_delta", 
  "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", 
  "region_100km_mean_elevation", "region_20km_mean_elevation")

birdlife_city_data_fixed_single_scale <- birdlife_city_data_fixed[,exclude_birdlife]
birdlife_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density", "open_forest", "happiness_future_life")])
```

"population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water"

```{r}
either_city_data <- fetch_city_data_for('either')
either_city_data
```

```{r}
either_city_data_fixed <- rfImpute(response ~ ., either_city_data)
either_city_data_fixed
```

```{r}
select_variables_from_random_forest(either_city_data_fixed)
```

```{r}
exclude_either <- !names(either_city_data_fixed) %in% c(
  "region_50km_cultivated", "region_20km_cultivated", 
  "region_50km_average_pop_density", "region_100km_average_pop_density", 
  "region_20km_elevation_delta", "region_100km_elevation_delta", 
  "region_50km_urban", "region_100km_urban", 
  "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", 
  "region_100km_mean_elevation", "region_50km_mean_elevation")

either_city_data_fixed_single_scale <- either_city_data_fixed[,exclude_either]
either_city_data_fixed_single_scale
```
```{r}
select_variables_from_random_forest(either_city_data_fixed_single_scale)
```


```{r}
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta", "")])
```

"either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm"

```{r}
both_city_data <- fetch_city_data_for('both')
both_city_data
```

```{r}
both_city_data_fixed <- rfImpute(response ~ ., both_city_data)
both_city_data_fixed
```

```{r}
select_variables_from_random_forest(both_city_data_fixed)
```

```{r}
exclude_both <- !names(both_city_data_fixed) %in% c(
  "region_50km_cultivated", "region_20km_cultivated", 
  "region_50km_urban", "region_100km_urban", 
  "region_100km_elevation_delta", "region_20km_elevation_delta", 
  "region_100km_average_pop_density", "region_50km_average_pop_density", 
  "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", 
  "region_100km_mean_elevation", "region_50km_mean_elevation")

both_city_data_fixed_single_scale <- both_city_data_fixed[,exclude_both]
both_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(both_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm")])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population"),])
create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population", "share_of_population_within_400m_of_open_space"),])
```

"both_pool_size", "temperature_annual_average", "happiness_negative_effect"


------------------------------------------
So....
------------------------------------------
"merlin_pool_size", "realm"
"population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water"
"either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm"
"both_pool_size", "temperature_annual_average", "temperature_monthly_min"

```{r}
summary(lm(response ~ merlin_pool_size, merlin_city_data_fixed))
summary(lm(response ~ birdlife_pool_size, birdlife_city_data_fixed))
summary(lm(response ~ either_pool_size, either_city_data_fixed))
summary(lm(response ~ both_pool_size, both_city_data_fixed))
```

```{r}
ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = response), both_city_data_fixed, color = "purple")
```

```{r}
summary(lm(response ~ region_100km_cultivated, merlin_city_data_fixed))
summary(lm(response ~ region_100km_cultivated, birdlife_city_data_fixed))
summary(lm(response ~ region_100km_cultivated, either_city_data_fixed))
summary(lm(response ~ region_100km_cultivated, both_city_data_fixed))
```


```{r}
ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_100km_cultivated), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_100km_cultivated), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_100km_cultivated), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_100km_cultivated), both_city_data_fixed, color = "purple")
```

```{r}
ggplot() + 
  geom_point(aes(x = population_growth, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = population_growth, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = population_growth, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = population_growth, y = response), both_city_data_fixed, color = "purple")
```
```{r}
ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_20km_average_pop_density), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_20km_average_pop_density), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_20km_average_pop_density), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_20km_average_pop_density), both_city_data_fixed, color = "purple")
```

```{r}
summary(lm(response ~ population_growth, merlin_city_data_fixed))
summary(lm(response ~ population_growth, birdlife_city_data_fixed))
summary(lm(response ~ population_growth, either_city_data_fixed))
summary(lm(response ~ population_growth, both_city_data_fixed))
```

```{r}
summary(lm(response ~ rainfall_monthly_min, merlin_city_data_fixed))
summary(lm(response ~ rainfall_monthly_min, birdlife_city_data_fixed))
summary(lm(response ~ rainfall_monthly_min, either_city_data_fixed))
summary(lm(response ~ rainfall_monthly_min, both_city_data_fixed))
```
```{r}
ggplot() + 
  geom_point(aes(x = rainfall_monthly_min, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = rainfall_monthly_min, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = rainfall_monthly_min, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = rainfall_monthly_min, y = response), both_city_data_fixed, color = "purple")
```
```{r}
ggplot() + 
  geom_point(aes(x = temperature_annual_average, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = temperature_annual_average, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = temperature_annual_average, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = temperature_annual_average, y = response), both_city_data_fixed, color = "purple")
```


```{r}
ggplot() + 
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), both_city_data_fixed, color = "purple")
```


```{r}
ggplot() + 
  geom_point(aes(x = happiness_negative_effect, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = happiness_negative_effect, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = happiness_negative_effect, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = happiness_negative_effect, y = response), both_city_data_fixed, color = "purple")
```

```{r}
ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), merlin_city_data_fixed)
```
```{r}
ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), birdlife_city_data_fixed)
```

```{r}
ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), either_city_data_fixed)
```

```{r}
ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), both_city_data_fixed)
```

```{r}
summary(lm(response ~ biome_name, merlin_city_data_fixed))
```


-----------------------------
In Summary
-----------------------------
Response is related to number of species in regional pool, the more species, the less the percentage of species in the city. Indicating a fixed number of species are able to move into cities.
The size of the regional pool is correlated with both the amount of urban and cultivated land cover, both reduce species in the regional pool.

Response is also lower in wet biomes and areas of the world, this is seen through the higher rainfall in the month with least rainfall in the year, and lower percentages in wetter biomes such as flooded grasslands and moist broadleaf forests.

Finally cities with a higher proportion of green public space are less likely to have a low response.

```{r}
merlin_city_data_2 <- fetch_city_data_for('merlin', T)
birdlife_city_data_2 <- fetch_city_data_for('birdlife', T)
```

```{r}
merlin_by_pool_size.lm <- lm(response ~ merlin_pool_size, merlin_city_data)
merlin_city_data$residuals_pool_size <- resid(merlin_by_pool_size.lm)

birdlife_by_pool_size.lm <- lm(response ~ birdlife_pool_size, birdlife_city_data)
birdlife_city_data$residuals_pool_size <- resid(birdlife_by_pool_size.lm)
```

```{r}
merlin_by_selected.lm <- lm(response ~ merlin_pool_size + realm, merlin_city_data)
merlin_city_data$residuals_selected <- resid(merlin_by_selected.lm)

birdlife_by_selected.lm <- lm(response ~ birdlife_pool_size + population_growth + region_100km_cultivated + percentage_urban_area_as_open_public_spaces + biome_name + rainfall_monthly_min + region_20km_average_pop_density + permanent_water, birdlife_city_data_fixed)
birdlife_city_data$residuals_selected <- resid(birdlife_by_selected.lm)
```

```{r}
merlin_by_all.lm <- lm(response ~ merlin_pool_size + realm + population_growth + region_100km_cultivated + percentage_urban_area_as_open_public_spaces + biome_name + rainfall_monthly_min + region_20km_average_pop_density+ permanent_water + population_growth + temperature_annual_average + temperature_monthly_min, data = merlin_city_data_fixed)
merlin_city_data$residuals_all <- resid(merlin_by_all.lm)

birdlife_by_all.lm <- lm(response ~ birdlife_pool_size + realm + population_growth + region_100km_cultivated + percentage_urban_area_as_open_public_spaces + biome_name + rainfall_monthly_min + region_20km_average_pop_density+ permanent_water + population_growth + temperature_annual_average + temperature_monthly_min, data = birdlife_city_data_fixed)
birdlife_city_data$residuals_all <- resid(birdlife_by_all.lm)
```

```{r}
merlin_by_preferred.lm <- lm(response ~ merlin_pool_size + biome_name + rainfall_monthly_min, data = merlin_city_data_fixed)
merlin_city_data$residuals_preferred <- resid(merlin_by_preferred.lm)

birdlife_by_preferred.lm <- lm(response ~ birdlife_pool_size + biome_name + rainfall_monthly_min, data = birdlife_city_data_fixed)
birdlife_city_data$residuals_preferred <- resid(birdlife_by_preferred.lm)
```

```{r}
ordered_cities <- data.frame(
  ranked_performance = 1:nrow(merlin_city_data_2),
  merlin_base_response = merlin_city_data_2$name[order(-merlin_city_data$response)],
  birdlife_base_response = birdlife_city_data_2$name[order(-birdlife_city_data$response)],
  merlin_model_pool_size_residuals = merlin_city_data_2$name[order(-merlin_city_data$residuals_pool_size)],
  birdlife_model_pool_size_residuals = birdlife_city_data_2$name[order(-birdlife_city_data$residuals_pool_size)],
  merlin_model_selected_residuals = merlin_city_data_2$name[order(-merlin_city_data$residuals_selected)],
  birdlife_model_selected_residuals = birdlife_city_data_2$name[order(-birdlife_city_data$residuals_selected)],
  merlin_model_all_residuals = merlin_city_data_2$name[order(-merlin_city_data$residuals_all)],
  birdlife_model_all_residuals = birdlife_city_data_2$name[order(-birdlife_city_data$residuals_all)],
  merlin_model_preferred_residuals = merlin_city_data_2$name[order(-merlin_city_data$residuals_preferred)],
  birdlife_model_preferred_residuals = birdlife_city_data_2$name[order(-birdlife_city_data$residuals_preferred)]
)
ordered_cities
```

```{r}
write_csv(ordered_cities, "city_effect_residuals.csv")
```

```{r}
library(ggrepel)
```

```{r}
merlin_city_data$name <- merlin_city_data_2$name
plot_merlin_poolsize <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_pool_size), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size")
plot_merlin_poolsize
```

```{r}
birdlife_city_data$name <- birdlife_city_data_2$name
plot_birdlife_poolsize <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_pool_size), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size")
plot_birdlife_poolsize
```

```{r}
plot_merlin_preferred <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_preferred), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size, biome, and rainfall")
plot_merlin_preferred
```

```{r}
plot_birdlife_preferred <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals_preferred), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size, biome, and rainfall")
plot_birdlife_preferred
```

```{r}
library(ggpubr)
```


```{r}
plot_residuals <- ggarrange(plot_merlin_poolsize, plot_birdlife_poolsize, plot_merlin_preferred, plot_birdlife_preferred)
plot_residuals
```
```{r}
jpeg("city_effect_residuals.jpg", width = 1600, height = 1200)
plot_residuals
dev.off()
```

```{r}
merlin_city_data_200 <- merlin_city_data[merlin_city_data$merlin_pool_size > 190 & merlin_city_data$merlin_pool_size < 210,]
merlin_city_data_200[order(merlin_city_data_200$response), c("name", "response")]
```


```{r}
birdlife_city_data_200 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 190 & birdlife_city_data$birdlife_pool_size < 210,]
birdlife_city_data_200[order(birdlife_city_data_200$response), c("name", "response")]
```

```{r}
birdlife_city_data_300 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 290 & birdlife_city_data$birdlife_pool_size < 310,]
birdlife_city_data_300[order(birdlife_city_data_300$response), c("name", "response")]
```
```{r}
merlin_city_data_300 <- merlin_city_data[merlin_city_data$merlin_pool_size > 290 & merlin_city_data$merlin_pool_size < 310,]
merlin_city_data_300[order(merlin_city_data_300$response), c("name", "response")]
```

```{r}
merlin_city_data_200$label = "200 (Merlin)"
merlin_city_data_200$pool_size = merlin_city_data_200$merlin_pool_size
merlin_city_data_300$label = "300 (Merlin)"
merlin_city_data_300$pool_size = merlin_city_data_300$merlin_pool_size
birdlife_city_data_200$label = "200 (Birdlife)"
birdlife_city_data_200$pool_size = birdlife_city_data_200$birdlife_pool_size
birdlife_city_data_300$label = "300 (Birdlife)"
birdlife_city_data_300$pool_size = birdlife_city_data_300$birdlife_pool_size
city_data_subset <- rbind(merlin_city_data_200[,c("name", "response", "pool_size", "label", "biome_name")], merlin_city_data_300[,c("name", "response", "pool_size", "label", "biome_name")], birdlife_city_data_200[,c("name", "response", "pool_size", "label", "biome_name")], birdlife_city_data_300[,c("name", "response", "pool_size", "label", "biome_name")])


ggplot(city_data_subset, aes(x = label, y = response, color = label)) + 
  geom_label_repel(aes(label = name), size = 3) + geom_point() +
  theme_bw() + theme(legend.position = "none") + xlab("Pool size (Pool)") + ylab("Random Effect Response")
```
```{r}
ggplot(city_data_subset, aes(x = label, y = response, color = label)) + 
  geom_label_repel(aes(label = biome_name), size = 3) + geom_point() +
  theme_bw() + theme(legend.position = "none") + xlab("Pool size (Pool)") + ylab("Random Effect Response")
```
```{r}
table(city_data$biome_name)
```

```{r}
summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + merlin_pool_size, merlin_city_data, family = "gaussian"))
```

```{r}
summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + birdlife_pool_size, birdlife_city_data, family = "gaussian"))
```

```{r}
unique(city_data$biome_name)
```

```{r}
split_biome <- function(dataset) {
  dataset$biome_vegetation <- 'Forests'
  dataset$biome_vegetation[dataset$biome_name == 'Tropical & Subtropical Grasslands, Savannas & Shrublands'] <- 'Grassland & Shrublands'
  dataset$biome_vegetation[dataset$biome_name == 'Montane Grasslands & Shrublands'] <- 'Grassland & Shrublands'
  dataset$biome_vegetation[dataset$biome_name == 'Temperate Grasslands, Savannas & Shrublands'] <- 'Grassland & Shrublands'
  dataset$biome_vegetation[dataset$biome_name == 'Flooded Grasslands & Savannas'] <- 'Grassland & Shrublands'
  dataset$biome_vegetation[dataset$biome_name == 'Deserts & Xeric Shrublands'] <- 'Grassland & Shrublands'
  
  dataset$biome_vegetation <- as.factor(dataset$biome_vegetation)
  
  dataset$biome_location <- 'Tropical & Subtropical'
  dataset$biome_location[dataset$biome_name == 'Montane Grasslands & Shrublands'] <- 'Montane'
  dataset$biome_location[dataset$biome_name == 'Mediterranean Forests, Woodlands & Scrub'] <- 'Mediterranean'
  dataset$biome_location[dataset$biome_name == 'Temperate Broadleaf & Mixed Forests'] <- 'Temperate'
  dataset$biome_location[dataset$biome_name == 'Temperate Grasslands, Savannas & Shrublands'] <- 'Temperate'
  dataset$biome_location[dataset$biome_name == 'Temperate Conifer Forests'] <- 'Temperate'
  dataset$biome_location[dataset$biome_name == 'Deserts & Xeric Shrublands'] <- 'Desert'
  dataset$biome_location[dataset$biome_name == 'Boreal Forests/Taiga'] <- 'Boreal'
  dataset$biome_location[dataset$biome_name == 'Flooded Grasslands & Savannas'] <- 'Global'
  
  dataset$biome_location <- as.factor(dataset$biome_location)
  
  dataset$biome_climate <- 'Normal'
  dataset$biome_climate[dataset$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'] <- 'Wet'
  dataset$biome_climate[dataset$biome_name == 'Flooded Grasslands & Savannas'] <- 'Wet'
  dataset$biome_climate[dataset$biome_name == 'Tropical & Subtropical Dry Broadleaf Forests'] <- 'Dry'
  dataset$biome_climate[dataset$biome_name == 'Deserts & Xeric Shrublands'] <- 'Dry'
  
  dataset$biome_climate <- as.factor(dataset$biome_climate)
  
  dataset
}
```


```{r}
tmp <- split_biome(merlin_city_data)
summary(glm(response ~ biome_vegetation + biome_location + biome_climate + merlin_pool_size, tmp, family = "gaussian"))
```

```{r}
summary(glm(response ~ biome_name + rainfall_monthly_min * merlin_pool_size, tmp, family = "gaussian"))
```

```{r}
library(boot)
```


```{r}
rsq <- function(formula, data, indices) {
  d <- data[indices,] # allows boot to select sample
  fit <- glm(formula, data=d)
  with(summary(fit), 1 - deviance/null.deviance)
} 

results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + merlin_pool_size)
boot.ci(results, type="bca")
results
```

```{r}
results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + rainfall_monthly_min * merlin_pool_size)
boot.ci(results, type="bca")
results
```

```{r}
results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ merlin_pool_size)
boot.ci(results, type="bca")
results
```

